Policy networks are widely used by political scientists and economists to explain various financial and social phenomena, such as the development of partnerships between political entities or institutions from different levels of governance. The analysis of policy networks demands a series of arduous and time-consuming manual steps including interviews and questionnaires. In this paper, we estimate the strength of relations between actors in policy networks using features extracted from data harvested from the web. Features include webpage counts, outlinks, and lexical information extracted from web documents or web snippets. The proposed approach is automatic and does not require any external knowledge source, other than the specification of the word forms that correspond to the political actors. The features are evaluated both in isolation and jointly for both positive and negative (antagonistic) actor relations. The proposed algorithms are evaluated on two EU policy networks from the political science literature. Performance is measured in terms of correlation and mean square error between the human rated and the automatically extracted relations. Correlation of up to 0.74 is achieved for positive relations. The extracted networks are validated by political scientists and useful conclusions about the evolution of the networks over time are drawn.